A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data

Ali Ebrahimnejad, Madjid Tavana, Seyed Hadi Nasseri () and Omid Gholami ()
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Ali Ebrahimnejad: Department of Mathematics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
Madjid Tavana: Business Systems and Analytics Department, La Salle University, Philadelphia, PA 19141, USA3Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, D-33098 Paderborn, Germany
Seyed Hadi Nasseri: Department of Mathematics, University of Mazandaran, Babolsar, Iran
Omid Gholami: Department of Mathematics, University of Mazandaran, Babolsar, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 01, 147-170

Abstract: Data envelopment analysis (DEA) is a widely used mathematical programming technique for measuring the relative efficiency of decision-making units which consume multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally used in classical DEA models, real-life problems often involve uncertainties characterized by fuzzy and/or random input and output data. We present a new input-oriented dual DEA model with fuzzy and random input and output data and propose a deterministic equivalent model with linear constraints to solve the model. The main contributions of this paper are fourfold: (1) we extend the concept of a normal distribution for fuzzy stochastic variables and propose a DEA model for problems characterized by fuzzy stochastic variables; (2) we transform the proposed DEA model with fuzzy stochastic variables into a deterministic equivalent linear form; (3) the proposed model which is linear and always feasible can overcome the nonlinearity and infeasibility in the existing fuzzy stochastic DEA models; (4) we present a case study in the banking industry to exhibit the applicability of the proposed method and feasibility of the obtained solutions.

Keywords: Data envelopment analysis; fuzzy random variable; normal distribution; banking industry (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622018500396

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